"Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics

TL;DR

This paper proposes a privacy-preserving AI system for mask recognition during pandemics, balancing high detection accuracy with strict privacy regulations, and evaluates different implementation options.

Contribution

It introduces a conceptual deep-learning AI for mask detection that maintains privacy, and analyzes the trade-offs between privacy levels and detection performance.

Findings

Detection accuracy between 95% and 99% in privacy-preserving settings

Different implementation options evaluated for performance and privacy trade-offs

Framework addresses GDPR compliance in surveillance applications

Abstract

The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure. In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV. However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, but also ensure that no privacy issues are introduced, as surveillance is a deterrent for citizens and regulations like General Data Protection Regulation (GDPR) demand strict regulations of such personal data. In this work, we show how a privacy-preserving mask recognition artifact could look like, demonstrate different options for implementation and evaluate performances. Our conceptual deep-learning based Artificial Intelligence is able to achieve detection performances between 95% and 99% in a privacy-friendly setting. On that basis, we elaborate on the trade-off between the level of privacy preservation and Artificial Intelligence performance, i.e. the "price of privacy".